Information complexity criteria for detecting influential observations in dynamic multivariate linear models using the genetic algorithm

DOI10.1016/S0378-3758(02)00461-5zbMath1011.62090OpenAlexW2078409416MaRDI QIDQ1874085

Peter Bearse, Hamparsum Bozdogan

Publication date: 22 May 2003

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(02)00461-5

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84) Statistical aspects of information-theoretic topics (62B10)

Related Items (7)

An efficient branch-and-bound strategy for subset vector autoregressive model selection ⋮ Robust modelling of periodic vector autoregressive time series ⋮ Estimating all possible SUR models with permuted exogenous data matrices derived from a VAR process ⋮ Performance of Model Selection Criteria in Bayesian Threshold VAR (TVAR) Models ⋮ Genetic algorithms for outlier detection in multiple regression with different information criteria ⋮ Model Selection in a System of Simultaneous Equations Model ⋮ A diagnostic of influential cases based on the information complexity criteria in generalized linear mixed models

Cites Work

• On two methods of identifying influential sets of observations
• Statistical information and likelihood. A collection of critical essays by Dr. D. Basu
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• A unifying representation of some case-deletion influence measures in univariate and multivariate linear regression
• On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models
• Influential Observations and Outliers in Regression
• General Classes of Influence Measures for Multivariate Regression
• Detection of Influential Observation in Linear Regression
• Influential Observations in Linear Regression
• Measures and procedures for the identification of locally influential observations in linear regression
• CASE‐DELETION DIAGNOSTICS FOR TEST STATISTICS IN MULTIVARIATE REGRESSION
• On Information and Sufficiency
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